Simon Koch

This course is an introduction to modern data science. Data science is the study of how to extract actionable, non-trivial knowledge from data. The proposed course will focus both on the software tools used by practitioners of modern data science, as well as the mathematical and statistical models that are employed in conjunction with such software tools. On the tools side, we will cover the basics of relational database systems, as well as modern systems for distributed computing based on MapReduce. On the models side, the course will cover standard supervised and unsupervised models for data analysis and pattern discovery. Can be taken concurrently with COMP 215. Cross-list: COMP 543. Mutually Exclusive: Cannot register for COMP 330 if student has credit for COMP 543/DSCI 302.

This course is an introduction to modern data science. Data science is the study of how to extract actionable, non-trivial knowledge from data. The course will focus on the software tools used by practitioners of modern data science, the mathematical and statistical models that are employed in conjunction with such software tools and the applications of these tools and systems to different problems and domains. On the tools side, we will cover the basics of relational database systems, as well as modern systems for manipulating large data sets such as Hadoop MapReduce, Apache Spark, and Google’s TensorFlow. On the model side, the course will cover standard supervised and unsupervised models for data analysis and pattern discovery. Mathematical sophistication (calculus, statistics) and programming skills that would be acquired in an undergraduate computer science program are expected. Most programming will be in Python and SQL. (SQL is covered in the course) with some Java. Cross-list: COMP 330. Mutually Exclusive: Cannot register for COMP 543 if student has credit for COMP 330.

An introduction to computer programming designed to give an overview of programming and algorithmic topics commonly seen in Data Science, such creating and manipulating data structures, graphs, dynamic programming, sorting and heuristic search algorithms. Students learn how to think about these problems and how to structure effective solutions to them using Python. No prior programming knowledge is required or expected. In order to enroll in an online section of this course, you are expected to have a working camera and microphone. During class sessions, you must be able to participate using your microphone and you are expected to have your camera on for the duration of the class so that you are visible to the instructor and other students in the class, just as you would be in an in-person class.